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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front Comput Sci    2012, Vol. 6 Issue (5) : 621-629    https://doi.org/10.1007/s11704-012-2959-0
RESEARCH ARTICLE
A co-evolving memetic wrapper for prediction of patient outcomes in TCM informatics
Dion DETTERER1, Paul KWAN1(), Cedric GONDRO2
1. School of Science and Technology, University of New England, Armidale NSW 2351, Australia; 2. The Centre for Genetic Analysis and Applications, University of New England, Armidale NSW 2351, Australia
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Abstract

Traditional Chinese medicine (TCM) relies on the combined effects of herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of these combined effects can become computationally intractable. Thus feature selection has become increasingly crucial as a pre-processing step prior to the study of combined effects in TCM informatics. In accord with this goal, a new feature selection algorithm known as a co-evolving memetic wrapper (COW) is proposed in this paper. COW takes advantage of recent research in genetic algorithms (GAs) and memetic algorithms (MAs) by evolving appropriate feature subsets for a given domain. Our empirical experiments have demonstrated that COW is capable of selecting subsets of herbs from a TCM insomnia dataset that shows signs of combined effects on the prediction of patient outcomes measured in terms of classification accuracy. We compare the proposed algorithm with results from statistical analysis including main effects and up to three way interaction terms and show that COW is capable of correctly identifying the herbs and herb by herb effects that are significantly associated to patient outcome prediction.

Keywords genetic algorithm      memetic algorithm      wrapper      feature selection      traditional Chinese medicine (TCM)      informatics     
Corresponding Author(s): KWAN Paul,Email:paul.kwan@une.edu.au   
Issue Date: 01 October 2012
 Cite this article:   
Dion DETTERER,Paul KWAN,Cedric GONDRO. A co-evolving memetic wrapper for prediction of patient outcomes in TCM informatics[J]. Front Comput Sci, 2012, 6(5): 621-629.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-012-2959-0
https://academic.hep.com.cn/fcs/EN/Y2012/V6/I5/621
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